• Korean System Dynamics Review
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• v.2 no.2
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• pp.25-40
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• 2001

This paper describes the forecast of power plant construction in a competitive korean electricity market. In Korea, KEPCO (Korea Electric Power Corporation, fully controlled by government) was responsible for from the production of the electricity to the sale of electricity to customer. However, the generation part is separated from KEPCO and six generation companies were established for whole sale competition from April 1st, 2001. The generation companies consist of five fossil power companies and one nuclear power company in Korea at present time. Fossil power companies are scheduled to be sold to private companies including foreign investors. Nuclear power company is owned and controlled by government. The competition in generation market will start from 2003. ISO (Independence System Operator will purchase the electricity from the power exchange market. The market price is determined by the SMP(System Marginal Price) which is decided by the balance between demand and supply of electricity in power exchange market. Under this uncertain circumstance, the energy policy planners such as government are interested to the construction of the power plant in the future. These interests are accelerated due to the recent shortage of electricity supply in California. In the competitive market, investors are no longer interested in the investment for the capital intensive, long lead time generating technologies such as nuclear and coal plants. Large unclear and coal plants were no longer the top choices. Instead, investors in the competitive market are interested in smaller, more efficient, cheaper, cleaner technologies such as CCGT(Combined Cycle Gas Turbine). Electricity is treated as commodity in the competitive market. The investors behavior in the commodity market shows that the new investment decision is made when the market price exceeds the sum of capital cost and variable cost of the new facility and the existing facility utilization depends on the marginal cost of the facility. This investors behavior can be applied to the new investments for the power plant. Under these postulations, there is the potential for power plant construction to appear in waves causing alternating periods of over and under supply of electricity like commodity production or real estate production. A computer model was developed to sturdy the possibility that construction will appear in waves of boom and bust in Korean electricity market. This model was constructed using System Dynamics method pioneered by Forrester(MIT, 1961) and explained in recent text by Sternman (Business Dynamics, MIT, 2000) and the recent work by Andrew Ford(Energy Policy, 1999). This model was designed based on the Energy Policy results(Ford, 1999) with parameters for loads and resources in Korea. This Korea Market Model was developed and tested in a small scale project to demonstrate the usefulness of the System Dynamics approach. Korea electricity market is isolated and not allowed to import electricity from outsides. In this model, the base load such as unclear and large coal power plant are assumed to be user specified investment and only CCGT is selected for new investment by investors in the market. This model may be used to learn if government investment in new unclear plants could compensate for the unstable actions of private developers. This model can be used to test the policy focused on the role of unclear investments over time. This model also can be used to test whether the future power plant construction can meet the government targets for the mix of generating resources and to test whether to maintain stable price in the spot market.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

• Archives of design research
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• v.11 no.3
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• pp.149-162
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• 1998

This study is what tried to examine the importance of material in industrial design. Then, if planning is to start of product design, the use of material is the last step to complete design. Design material has existed from the time before mankind were born, and the new material which is useful for human beings is developing rapidly. It is no exaggeration to say that our environment is the aggregation of material which was surrounded with us. Then, material has the timely, spatial and cultural feature as well as physical feature. Besides, all sorts of functions of communicating information are being contained in accordance with the character of material. The outside surface of material has the function to develop the sense organ of human beings. This study examines 4 kinds of cultural features in design materials and shown by findings is as follows : Rrst is the technical progressivity to lead new Design form. Second is the symbolic nature to perform the communication function. Third is the sensible attribute to develop surface effect of Design material. Fourth is the future-oriented nature to convince the future such as new material and technology etc. Thus, so as to perform the competitive product design, it is important to grasp the harmony between material and design, structure and processing, and the substantial meaning that the material has and apply them properly, above all. Because the discovery of material will be the measure to forecast future design.

• KDI Journal of Economic Policy
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• v.35 no.4
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• pp.31-61
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• 2013

The purpose of this study is to calculate the economic value of transport demand forecasting risks in the road PPP project. Under the assumption that volatility of the road PPP project value occurs only in regard with uncertainty of traffic volume forecasting, this study calculates the economic value of the traffic forecasting risks in the case of the road PPP project. To that end, forecasted traffic volume is assumed to be a stochastic variable and to follow the Geometric Brownian motion as time passes. In particular, this study attempts to differentiate itself from existing studies that simply use an arbitrary assumption by presenting the application of different traffic volume growth volatility and the rates before and after the ramp-up period. Analysis of the case projects reveals that the risk premium related to traffic volume forecast of the project turns out as 7.39~8.30%, without considering option value-such as minimum revenue guarantee-while the project value volatility caused by transport demand forecasting risks is 17.11%. As the discount rate grows higher, the project value volatility tends to decrease and volatility in project value is always suggested to be larger than that in transport volume influenced by leverage effect due to fixed expenditure. The market value of transport demand forecasting risk-calculated using the project value volatility and risk premium-is analyzed to be between 0.42~0.50, implying that a 1% increase or decrease in the transport amount volatility would lead to a 0.42~0.50% increase or decrease in risk premium of the project.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.49-67
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• 2006

This study examines whether won/dollar futures have price discovery function and volatility spillover effect or not, using intraday won/dollar futures prices, volumes, and spot rates for the interval from March 2, 2005 through May 30, 2005. Futures prices and spot rates are non-stationary, but there is the cointegration relationship between two time series. Futures returns, spot returns, and volumes are stationary. Asymmetric effects on volatility in futures returns and spot returns does not exist. Analytical results of mean equations of the BGARCH-EC (bivariate GARCH-error correction) model show that the increase of futures returns raise spot returns after 5 minutes, which implies that futures returns lead spot returns and won/dollar futures have price discovery function. In addition, the long-run equilibrium relationship between the two returns could help forecast spot returns. Analytical results of variance equations indicate that short-run innovations in the futures market positively affect the conditional variances of spot returns, that is, there is the volatility spillover effect in the won/dollar futures market. A dummy variable of volumes does not have an effect on two returns but influences significantly on two conditional variances.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.407-417
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• 2024

The objective of this study is to predict and reduce potential damage caused by chlorine gas leaks, a hazardous material, when vehicles transporting it overturn due to accidents or other incidents. The goal is to forecast the anticipated damages caused by chlorine toxicity levels (ppm) and to design effective response strategies for mitigating them. To predict potential damages, we conducted quantitative assessments using the ALOHA program to calculate the toxic effects (ppm) and damage distances resulting from chlorine leaks, taking into account potential negligence of drivers during transportation. The extent of damage from toxic gas leaks is influenced by various factors, including the amount of the leaked hazardous material and the meteorological conditions at the time of the leak. Therefore, a comprehensive analysis of damage distances was conducted by examining various scenarios that involved variations in the amount of leakage and weather conditions. Under intermediate conditions (leakage quantity: 5 tons, wind speed: 3 m/s, atmospheric stability: D), the estimated distance for exceeding the AEGL-2 level of 2 ppm was calculated to be 9 km. This concentration poses a high risk of respiratory disturbance and potential human casualties, comparable to the toxicity of hydrogen chloride. In particular, leaks in urban areas can lead to significant loss of life. In the event of a leakage incident, we proposed a plan to minimize damage by implementing appropriate response strategies based on the location and amount of the leak when an accident occurs.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of the Daesoon Academy of Sciences
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• v.35
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• pp.109-145
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• 2020

Jeungsan, Kang Il-Sun (hereafter, Jeungsan)'s perspective on divine beings can be characterized by the philosophical notion of divinity, which recognizes a variety of divine entities. Jeungsan insisted that all things embrace divine entities. Furthermore, he claimed that the backgrounds of all incidents were influenced by these gods. Jeungsan thought that the universe consists of the heavenly realm, the earthly realm and the underground realm. He insisted that there were many gods in each realm. And Jeungsan defined his times as the era of divine beings, which meant that the age was a time for divine beings to actively interact with one another and take the lead in world affairs. Divine beings were briskly involved in human affairs and could either reciprocate gratitude or attain revenge. They were also divine beings that could change the acts and perception of humans as well as judge human acts. However, Jeungsan predicted that by the time the paradisiacal land of immortals was established in the Later World, divine beings would instead run errands for humans. In addition, he forecast that divine beings would be entities likely to harbor grievances just like humans, yet they would ultimately become perfected beings in the Later World. Jeungsan further suggested a multitude of various concepts such as the mutual relationship wherein the realm of divine beings and the realm of humanity interrelate with each other, the mutual responses and functions between them, mutual itineration, co-existence, and the homogeneity of divine beings and humans, which described how both have the same innate characteristics. Jeungsan proposed the concept that 'Divinity is an existential state experienced after one's death." In this regard, he is the one who formulated a new perspective of divinity. Moreover, Jeunsan stressed the immortality of humans (continuity or eternality) and the co-existence of divine beings and humans. He emphasized that divinity is intrinsically immanent and the realm of divine beings has a hierarchical system that maintains order and is akin to that of the human realm. Jeungsan recognized a revolutionary change and perspective based on humanity by suggesting a unique view of humanity. In other words, he was a religious figure who introduced an ingenious view of divinity and dramatically transformed this pattern of reasoning. In conclusion, Jeungsan re-interpreted traditional views of divinity in Korea and systemized them into a new concept of divinity in an ingenious way.